A new method for image segmentation based on BP neural network and gravitational search algorithm enhanced by cat chaotic mapping

IEEJ Transactions Elec Engng

Xie

et al. 2019

This article presents an innovative approach for fault diagnosis based on an optimized generalized regression neural network (GRNN) by integrating with dissolved gas analysis, cuckoo search algorithm (CSA), and rough set theory (RS). In the proposed method, the high dimensioned data will be simplified and reduced by RS to generate better features or attributes for the GRNN input. Meanwhile, to enhance the network performance, the smoothing factor of GRNN is optimized by CSA with Levy flight, which leads to a good global convergence. As a consequence, CSA can provide a good solution to effectively improve the fault diagnosis performance. To validate and demonstrate the proposed method, we applied it to a real‐world fault diagnosis application, power transformer fault diagnosis, by comparing the results with those of other methods. From the experimental results obtained from the evaluation, it is obvious that the proposed fault diagnosis method enabled with RS‐CSA‐GRNN can provide a useful solution for power transformer fault diagnosis because it outperformed other GRNN‐based methods that deployed different optimizing algorithms such as the particle swarm optimization and genetic algorithm. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

“…Here, the parameters of MVO, GSA, and GWO are the same as reported in refs. [22][23][24], respectively. However, there is a difference between MVO-MLP, GSA-BP, GWO-BP, and CSA-GRNN.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

An optimized GRNN‐enabled approach for power transformer fault diagnosis

IEEJ Transactions Elec Engng

Xie

et al. 2019

“…Back-propagation (BP) neural network is a multi-layer feed-forward neural network, which belongs to an uncertain nonlinear mathematical model [32,33,34]. The BP network consists of an input layer, hidden layer and output layer.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning-Based Sensor Data Modeling Methods for Power Transformer PHM

Dong

et al. 2018

Sensors

An emerging prognostic and health management (PHM) technology has recently attracted a great deal of attention from academies, industries, and governments. The need for higher equipment availability and lower maintenance cost is driving the development and integration of prognostic and health management systems. PHM models depend on the smart sensors and data generated from sensors. This paper proposed a machine learning-based methods for developing PHM models from sensor data to perform fault diagnostic for transformer systems in a smart grid. In particular, we apply the Cuckoo Search (CS) algorithm to optimize the Back-propagation (BP) neural network in order to build high performance fault diagnostics models. The models were developed using sensor data called dissolved gas data in oil of the power transformer. We validated the models using real sensor data collected from power transformers in China. The results demonstrate that the developed meta heuristic algorithm for optimizing the parameters of the neural network is effective and useful; and machine learning-based models significantly improved the performance and accuracy of fault diagnosis/detection for power transformer PHM.

“…The application of an artificial neural network makes the work of power transformer fault diagnosis more efficient. Domestic and foreign scholars have established diagnostic models for support vector machine (SVM) and fuzzy theory .…”

Section: Introductionmentioning

confidence: 99%

A Hybrid machine‐learning method for oil‐immersed power transformer fault diagnosis

IEEJ Transactions Elec Engng

Wen-kai

et al. 2020

This paper presents a hybrid machine‐learning method based on oil‐immersed power transformer fault diagnosis Probability Neural Network (PNN) optimized via a Multi‐Verse Optimizer (MVO) algorithm. PNN is a radial basis function prefeedback neural network based on Bayesian decision theory. It has strong fault tolerance and has significant advantages in pattern classification. However, the performance of PNN is greatly affected by the hidden‐layer unit‐smoothing factor, and the classification result is affected. MVO is a metaheuristic algorithm with strong global convergence. Therefore, the smoothing factor of MVO‐optimized PNN (MVO‐PNN) can effectively improve the fault diagnosis ability. Recent studies have demonstrated the MVO algorithm. We utilize an experiment about the oil data in the power transformer in Jiangxi Province, China. The results show that MVO‐PNN can significantly improve the accuracy of power transformer fault classification and is more efficient than the Cuckoo search algorithm, Bat algorithm, Genetic Algorithm optimization, and other algorithms capabilities in some cases. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.